Evaluation of Coupling Reversed Phase, Aqueous Normal Phase, and Hydrophilic Interaction Liquid Chromatography with Orbitrap Mass Spectrometry for Metabolomic Studies of Human Urine

Changes in the amount of nucleotide sugars in aged mouse tissues

Aging affects tissue glycan profiles, which may alter cellular functions and increase the risk of age-related diseases. Glycans are biosynthesized by glycosyltransferases using the corresponding nucleotide sugar, and the availability of nucleotide sugars affects glycosylation efficiency. However, the effects of aging on nucleotide sugar profiles and contents are yet to be elucidated. Therefore, this study aimed to investigate the effects of aging on nucleotide sugars using a new LC-MS/MS method. Specifically, the new method was used to determine the nucleotide sugar contents of various tissues (brain, liver, heart, skeletal muscle, kidney, lung, and colon) of male C57BL/6NCr mice (7- or 26-month-old). Characteristic age-associated nucleotide sugar changes were observed in each tissue sample. Particularly, there was a significant decrease in UDP-glucuronic acid content in the kidney of aged mice and a decrease in the contents of several nucleotide sugars, including UDP-N-acetylgalactosamine, in the brain of aged mice. Additionally, there were variations in nucleotide sugar profiles among the tissues examined regardless of the age. The kidneys had the highest concentration of UDP-glucuronic acid among the seven tissues. In contrast, the skeletal muscle had the lowest concentration of total nucleotide sugars among the tissues; however, CMP-N-acetylneuraminic acid and CDP-ribitol were relatively enriched. Conclusively, these findings may contribute to the understanding of the roles of glycans in tissue aging.

Metabolic priming by multiple enzyme systems supports glycolysis, HIF1α stabilisation, and human cancer cell survival in early hypoxia

Adaptation to chronic hypoxia occurs through changes in protein expression, which are controlled by hypoxia-inducible factor 1α (HIF1α) and are necessary for cancer cell survival. However, the mechanisms that enable cancer cells to adapt in early hypoxia, before the HIF1α-mediated transcription programme is fully established, remain poorly understood. Here we show in human breast cancer cells, that within 3 h of hypoxia exposure, glycolytic flux increases in a HIF1α-independent manner but is limited by NAD+ availability. Glycolytic ATP maintenance and cell survival in early hypoxia rely on reserve lactate dehydrogenase A capacity as well as the activity of glutamate-oxoglutarate transaminase 1 (GOT1), an enzyme that fuels malate dehydrogenase 1 (MDH1)-derived NAD+. In addition, GOT1 maintains low α-ketoglutarate levels, thereby limiting prolyl hydroxylase activity to promote HIF1α stabilisation in early hypoxia and enable robust HIF1α target gene expression in later hypoxia. Our findings reveal that, in normoxia, multiple enzyme systems maintain cells in a primed state ready to support increased glycolysis and HIF1α stabilisation upon oxygen limitation, until other adaptive processes that require more time are fully established.

Integrated Transcriptomic and Metabolomic Mapping Reveals the Mechanism of Action of Ceftazidime/Avibactam against Pan-Drug-Resistant Klebsiella pneumoniae

Here, we employed an integrated metabolomics and transcriptomics approach to investigate the molecular mechanism(s) of action of ceftazidime/avibactam against a pan-drug-resistant K. pneumoniae clinical isolate from a patient with urinary tract infection. Ceftazidime/avibactam induced time-dependent perturbations in the metabolome and transcriptome of the bacterium, mainly at 6 h, with minimal effects at 1 and 3 h. Metabolomics analysis revealed a notable reduction in essential lipids involved in outer membrane glycerolipid biogenesis. This disruption effect extended to peptidoglycan and lipopolysaccharide biosynthetic pathways, including lipid A and O-antigen assembly. Importantly, ceftazidime/avibactam not only affected the final steps of peptidoglycan biosynthesis in the periplasm, a common mechanism of ceftazidime action, but also influenced the synthesis of lipid-linked intermediates and early stages of cytoplasmic peptidoglycan synthesis. Furthermore, ceftazidime/avibactam substantially inhibited central carbon metabolism (e.g., the pentose phosphate pathway and tricarboxylic acid cycle). Consistently, the dysregulation of genes governing these metabolic pathways aligned with the metabolomics findings. Certain metabolomics and transcriptomics signatures associated with ceftazidime resistance were also perturbed. Consistent with the primary target of antibiotic activity, biochemical assays also confirmed the direct impact of ceftazidime/avibactam on peptidoglycan production. This study explored the intricate interactions of ceftazidime and avibactam within bacterial cells, including their impact on cell envelope biogenesis and central carbon metabolism. Our findings revealed the complexities of how ceftazidime/avibactam operates, such as hindering peptidoglycan formation in different cellular compartments. In summary, this study confirms the existing hypotheses about the antibacterial and resistance mechanisms of ceftazidime/avibactam while uncovering novel insights, including its impact on lipopolysaccharide formation.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

Importance of glutamine in synaptic vesicles revealed by functional studies of SLC6A17 and its mutations pathogenic for intellectual disability

Human mutations in the gene encoding the solute carrier (SLC) 6A17 caused intellectual disability (ID). The physiological role of SLC6A17 and pathogenesis of SLC6A17-based-ID were both unclear. Here, we report learning deficits in Slc6a17 knockout and point mutant mice. Biochemistry, proteomic, and electron microscopy (EM) support SLC6A17 protein localization in synaptic vesicles (SVs). Chemical analysis of SVs by liquid chromatography coupled to mass spectrometry (LC-MS) revealed glutamine (Gln) in SVs containing SLC6A17. Virally mediated overexpression of SLC6A17 increased Gln in SVs. Either genetic or virally mediated targeting of Slc6a17 reduced Gln in SVs. One ID mutation caused SLC6A17 mislocalization while the other caused defective Gln transport. Multidisciplinary approaches with seven types of genetically modified mice have shown Gln as an endogenous substrate of SLC6A17, uncovered Gln as a new molecule in SVs, established the necessary and sufficient roles of SLC6A17 in Gln transport into SVs, and suggested SV Gln decrease as the key pathogenetic mechanism in human ID.

Further Evaluation of the Base Stability of Hydrophilic Interaction Chromatography Columns Packed with Silica or Ethylene-Bridged Hybrid Particles

One of the fundamental attributes of a liquid chromatography column is its stability when exposed to acidic and basic mobile phases. However, there have been relatively few reports to date on the stability of hydrophilic interaction chromatography (HILIC) columns. Here, we report the results of stability evaluations carried out for HILIC columns packed with ethylene-bridged hybrid or silica particles using accelerated conditions, employing a 100% aqueous pH 11.3 ammonium bicarbonate mobile phase at 70 °C. Under these conditions, the primary mode of column failure was a loss of efficiency due to the formation of voids resulting from the hydrolysis of the particles. We investigated the dependence of stability on the surface area of both unbonded and sulfobetaine-bonded ethylene-bridged hybrid stationary phases. The results show a clear trend of stability increasing as the surface area decreases. Several commercially available HILIC columns that are recommended for use with high-pH mobile phases were also evaluated. The results show times to 50% loss of the initial efficiency ranging from 0.3 to 9.9 h. Columns containing unbonded, sulfobetaine-bonded or diol-bonded ethylene-bridged hybrid stationary phases had longer lifetimes than amino-bonded silica or sulfobetaine-bonded, hybrid-coated, superficially porous silica columns.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling

INTRODUCTION

This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.

METHOD

A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.

CONCLUSIONS

The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Integrated metabolomic and transcriptomic analyses of the synergistic effect of polymyxin-rifampicin combination against Pseudomonas aeruginosa

BACKGROUND

Understanding the mechanism of antimicrobial action is critical for improving antibiotic therapy. For the first time, we integrated correlative metabolomics and transcriptomics of Pseudomonas aeruginosa to elucidate the mechanism of synergistic killing of polymyxin-rifampicin combination.

METHODS

Liquid chromatography-mass spectrometry and RNA-seq analyses were conducted to identify the significant changes in the metabolome and transcriptome of P. aeruginosa PAO1 after exposure to polymyxin B (1 mg/L) and rifampicin (2 mg/L) alone, or in combination over 24 h. A genome-scale metabolic network was employed for integrative analysis.

RESULTS

In the first 4-h treatment, polymyxin B monotherapy induced significant lipid perturbations, predominantly to fatty acids and glycerophospholipids, indicating a substantial disorganization of the bacterial outer membrane. Expression of ParRS, a two-component regulatory system involved in polymyxin resistance, was increased by polymyxin B alone. Rifampicin alone caused marginal metabolic perturbations but significantly affected gene expression at 24 h. The combination decreased the gene expression of quorum sensing regulated virulence factors at 1 h (e.g. key genes involved in phenazine biosynthesis, secretion system and biofilm formation); and increased the expression of peptidoglycan biosynthesis genes at 4 h. Notably, the combination caused substantial accumulation of nucleotides and amino acids that last at least 4 h, indicating that bacterial cells were in a state of metabolic arrest.

CONCLUSION

This study underscores the substantial potential of integrative systems pharmacology to determine mechanisms of synergistic bacterial killing by antibiotic combinations, which will help optimize their use in patients.

MOG analogues to explore the MCT2 pharmacophore, α-ketoglutarate biology and cellular effects of N-oxalylglycine

α-ketoglutarate (αKG) is a central metabolic node with a broad influence on cellular physiology. The αKG analogue N-oxalylglycine (NOG) and its membrane-permeable pro-drug derivative dimethyl-oxalylglycine (DMOG) have been extensively used as tools to study prolyl hydroxylases (PHDs) and other αKG-dependent processes. In cell culture media, DMOG is rapidly converted to MOG, which enters cells through monocarboxylate transporter MCT2, leading to intracellular NOG concentrations that are sufficiently high to inhibit glutaminolysis enzymes and cause cytotoxicity. Therefore, the degree of (D)MOG instability together with MCT2 expression levels determine the intracellular targets NOG engages with and, ultimately, its effects on cell viability. Here we designed and characterised a series of MOG analogues with the aims of improving compound stability and exploring the functional requirements for interaction with MCT2, a relatively understudied member of the SLC16 family. We report MOG analogues that maintain ability to enter cells via MCT2, and identify compounds that do not inhibit glutaminolysis or cause cytotoxicity but can still inhibit PHDs. We use these analogues to show that, under our experimental conditions, glutaminolysis-induced activation of mTORC1 can be uncoupled from PHD activity. Therefore, these new compounds can help deconvolute cellular effects that result from the polypharmacological action of NOG.

Evaluation of the Base Stability of Hydrophilic Interaction Chromatography Columns Packed with Silica or Ethylene-Bridged Hybrid Particles

Stability as a function of mobile phase pH is an important consideration when selecting a chromatographic column. While the pH stability of reversed-phase columns is widely studied, there are relatively few reports of the stability of hydrophilic interaction chromatography (HILIC) columns. We evaluated the stability of silica and ethylene-bridged hybrid HILIC columns when used with mobile phases containing basic buffers. The predominant mode of column degradation observed in our studies was a decrease in efficiency due to voiding, resulting from the hydrolysis of the silica particles. Associated with this were increases in tailing factors. Retention factor changes were also noted but were smaller than the efficiency losses. The dependence of the rate of efficiency decrease on the key variables of temperature, mobile phase pH and water content were studied for an unbonded silica column. The effect of the acetonitrile concentration on the pH of the mixed aqueous/acetonitrile mobile phases was also investigated. Using conditions found to cause a 50% decrease in efficiency after approximately five hours of exposure to the basic solution, we evaluated eight different commercially available HILIC columns containing silica or ethylene-bridged hybrid particles. The results show large differences between the stability of the silica and ethylene-bridged hybrid particle stationary phases, with the latter exhibiting greater stability.

Characterization of a highly stable zwitterionic hydrophilic interaction chromatography stationary phase based on hybrid organic/inorganic particles

We have characterized a sulfobetaine stationary phase based on 1.7 μm ethylene-bridged hybrid organic/inorganic particles, which is intended for use in hydrophilic interaction chromatography. The efficiency of columns packed with this material were determined as a function of flow rate, demonstrating a minimum reduced plate height of 2.4. The batch-to-batch reproducibility was assessed using the separation of a mixture of acids, bases and neutrals. We compared the retention and selectivity of the hybrid sulfobetaine stationary phase to that of several benchmark materials. The hybrid sulfobetaine material gave strong retention for polar neutrals and high selectivity for methyl groups, hydroxy groups and configurational isomers. Large differences in cation and anion retention were observed among the columns. We characterized the acid and base stability of the hybrid sulfobetaine stationary phase, using accelerated tests at pH 1.3 and 11.0, both at 70°C. The results support a recommended pH range of 2 to 10. We also investigated the performance of columns packed with this material for metal-sensitive analytes, comparing conventional stainless steel column hardware to hardware that incorporates hybrid surface technology to mitigate interactions with metal surfaces. Compared to the conventional columns, the hybrid surface technology columns showed greatly improved peak shape. This article is protected by copyright. All rights reserved.

Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies

In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅_NND), the "asterisk equations" (A_O), and surface coverage by bins (SC_G), convex hulls (SC_CH), and α-convex hulls (SC_αH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SG_G, SC_αH with α = 0.1, and H̅_NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.

Synergy of the Polymyxin-Chloramphenicol Combination against New Delhi Metallo-β-Lactamase-Producing Klebsiella pneumoniae Is Predominately Driven by Chloramphenicol

Carbapenem-resistant Klebsiella pneumoniae has been classified as an Urgent Threat by the Centers for Disease Control and Prevention (CDC). The combination of two "old" antibiotics, polymyxin and chloramphenicol, displays synergistic killing against New Delhi metallo-β-lactamase (NDM)-producing K. pneumoniae. However, the mechanism(s) underpinning their synergistic killing are not well studied. We employed an in vitro pharmacokinetic/pharmacodynamic model to mimic the pharmacokinetics of the antibiotics in patients and examined bacterial killing against NDM-producing K. pneumoniae using a metabolomic approach. Metabolomic analysis was integrated with an isolate-specific genome-scale metabolic network (GSMN). Our results show that metabolic responses to polymyxin B and/or chloramphenicol against NDM-producing K. pneumoniae involved the inhibition of cell envelope biogenesis, metabolism of arginine and nucleotides, glycolysis, and pentose phosphate pathways. Our metabolomic and GSMN modeling results highlight the novel mechanisms of a synergistic antibiotic combination at the network level and may have a significant potential in developing precision antimicrobial chemotherapy in patients.

A mathematical method for calibrating the signal drift in liquid chromatography - mass spectrometry analysis

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become the most versatile analytical tool for profiling small-molecule compounds and increasingly been applied in many fields. Nevertheless, LC-MS based quantification still face some challenges, such as signal drift in LC-MS, which may affect the validity of the obtained data and lead to misinterpretation of biological results. Here, we established a calibration method known as "RIM" to compensate the signal drift of LC-MS. To this end, a mixture of d₄-2-dimethylaminoethylamine (d₄-DMED)-coded normal fatty acids (C5-C23) was used as calibrants to construct RIM calibration. With the addition of calibrants, not only the MS signal drift, but also the mass accuracy and LC retention time can be calibrated, thereby improving the reliability of quantitative data. The effectiveness of RIM was carefully validated using a human serum extract spiked with 34 standards and then RIM was applied for rat brain untargeted metabolome research. In addition, to expand the functionality and flexibility of RIM for data handling, we generated a MATLAB-based RIM program, which implements the above concepts and allows automatic data process. Taken together, the proposed RIM method has potential application in large-scale quantitative study of complex samples.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.

Metabolomics shows the Australian dingo has a unique plasma profile

Dingoes occupy a wide range of the Australian mainland and play a crucial role as an apex predator with a generalist omnivorous feeding behaviour. Dingoes are ecologically, phenotypically and behaviourally distinct from modern breed dogs and have not undergone artificial selection since their arrival in Australia. In contrast, humans have selected breed dogs for novel and desirable traits. First, we examine whether the distinct evolutionary histories of dingoes and domestic dogs has lead to differences in plasma metabolomes. We study metabolite composition differences between dingoes (n = 15) and two domestic dog breeds (Basenji n = 9 and German Shepherd Dog (GSD) n = 10). Liquid chromatography mass spectrometry, type II and type III ANOVA with post-hoc tests and adjustments for multiple comparisons were used for data evaluation. After accounting for within group variation, 62 significant metabolite differences were detected between dingoes and domestic dogs, with the majority of differences in protein (n = 14) and lipid metabolites (n = 12), mostly lower in dingoes. Most differences were observed between dingoes and domestic dogs and fewest between the domestic dog breeds. Next, we collect a second set of data to investigate variation between pure dingoes (n = 10) and dingo-dog hybrids (n = 10) as hybridisation is common in regional Australia. We detected no significant metabolite differences between dingoes and dingo-dog hybrids after Bonferroni correction. However, power analysis showed that increasing the sample size to 15 could result in differences in uridine 5′-diphosphogalactose (UDPgal) levels related to galactose metabolism. We suggest this may be linked to an increase in Amylase 2B copy number in hybrids. Our study illustrates that the dingo metabolome is significantly different from domestic dog breeds and hybridisation is likely to influence carbohydrate metabolism.

Rhythmic glucose metabolism regulates the redox circadian clockwork in human red blood cells

Circadian clocks coordinate mammalian behavior and physiology enabling organisms to anticipate 24-hour cycles. Transcription-translation feedback loops are thought to drive these clocks in most of mammalian cells. However, red blood cells (RBCs), which do not contain a nucleus, and cannot perform transcription or translation, nonetheless exhibit circadian redox rhythms. Here we show human RBCs display circadian regulation of glucose metabolism, which is required to sustain daily redox oscillations. We found daily rhythms of metabolite levels and flux through glycolysis and the pentose phosphate pathway (PPP). We show that inhibition of critical enzymes in either pathway abolished 24-hour rhythms in metabolic flux and redox oscillations, and determined that metabolic oscillations are necessary for redox rhythmicity. Furthermore, metabolic flux rhythms also occur in nucleated cells, and persist when the core transcriptional circadian clockwork is absent in Bmal1 knockouts. Thus, we propose that rhythmic glucose metabolism is an integral process in circadian rhythms.

Comparative metabolomics reveals key pathways associated with the synergistic activity of polymyxin B and rifampicin combination against multidrug-resistant Acinetobacter baumannii

Multidrug-resistant (MDR) Acinetobacter baumannii presents a critical challenge to human health worldwide and polymyxins are increasingly used as a last-line therapy. Due to the rapid emergence of resistance during polymyxin monotherapy, synergistic combinations (e.g. with rifampicin) are recommended to treat A. baumannii infections. However, most combination therapies are empirical, owing to a dearth of understanding on the mechanism of synergistic antibacterial killing. In the present study, we employed metabolomics to investigate the synergy mechanism of polymyxin B-rifampicin against A. baumannii AB5075, an MDR clinical isolate. The metabolomes of A. baumannii AB5075 were compared at 1 and 4 h following treatments with polymyxin B alone (0.75 mg/L, i.e. 3 × MIC), rifampicin alone (1 mg/L, i.e. 0.25 × MIC) and their combination. Polymyxin B monotherapy significantly perturbed glycerophospholipid and fatty acid metabolism at 1 h, reflecting its activity on bacterial outer membrane. Rifampicin monotherapy significantly perturbed glycerophospholipid, nucleotide and amino acid metabolism, which are related to the inhibition of RNA synthesis. The combination treatment significantly perturbed the metabolism of nucleotides, amino acids, fatty acids and glycerophospholipids at 1 and 4 h. Notably, the intermediate metabolite pools from pentose phosphate pathway were exclusively enhanced by the combination, while most metabolites from the nucleotide and amino acid biosynthesis pathways were significantly decreased. Overall, the synergistic activity of the combination was initially driven by polymyxin B which impacted pathways associated with outer membrane biogenesis; and subsequent effects were mainly attributed to rifampicin via the inhibition of RNA synthesis. This study is the first to reveal the synergistic killing mechanism of polymyxin-rifampicin combination against polymyxin-susceptible MDR A. baumannii at the network level. Our findings provide new mechanistic insights for optimizing this synergistic combination in patients.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Metabolomic Description of Ivacaftor Elevating Polymyxin B Mediated Antibacterial Activity in Cystic Fibrosis Pseudomonas aeruginosa

We have demonstrated that ivacaftor displays synergistic antibacterial activity in combination with polymyxin B against polymyxin-resistant Pseudomonas aeruginosa that commonly colonizes the lungs of people with cystic fibrosis (CF). However, the underlying mechanism(s) remain unclear. In the present study, we employed untargeted metabolomics to investigate the synergistic killing mechanism of polymyxin B in combination with ivacaftor against a polymyxin-susceptible P. aeruginosa FADDI-PA111 (polymyxin B MIC = 2 mg/L) and a polymyxin-resistant CF P. aeruginosa FADDI-PA006 (polymyxin B MIC = 8 mg/L). Metabolites were extracted at 3 h after treatments with polymyxin B alone (2 μg/mL for FADDI-PA111 and 4 μg/mL FADDI-PA006 P. aeruginosa), ivacaftor alone (8 μg/mL), and in combination. Polymyxin B monotherapy induced significant perturbations in the glycerophospholipid and fatty acid metabolism pathways against FADDI-PA111 and to a lesser extent in FADDI-PA006. In both strains, treatment with ivacaftor alone induced more pronounced perturbations in glycerophospholipid and fatty acid metabolism pathways than that with polymyxin B alone. This highlights the unique antimicrobial mode of action of ivacaftor. Pathway analysis revealed that in combination treatment, polymyxin B mediated killing is elevated by ivacaftor, largely due to the inhibition of cell envelope biogenesis via suppression of key membrane lipid metabolites (e.g., sn-glycerol 3-phosphate and sn-glycero-3-phosphoethanolamine) as well as perturbations in peptidoglycan and lipopolysaccharide biosynthesis. Furthermore, significant perturbations in the levels of amino sugars and nucleotide sugars, glycolysis, the tricarboxylic acid cycle, and pyrimidine ribonucleotide biogenesis were observed with the combination treatment. These findings provide novel mechanistic information on the synergistic antibacterial activity of polymyxin-ivacaftor combination.

A complex matrix characterization approach, applied to cigarette smoke, that integrates multiple analytical methods and compound identification strategies for non-targeted liquid chromatography with high-resolution mass spectrometry

RATIONALE

For the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.

METHODS

We applied a combination of four chromatographic/ionization techniques (reversed-phase (RP) - heated electrospray ionization (HESI) in both positive (+) and negative (-) modes, RP - atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) - HESI positive) using a Thermo Q Exactive™ liquid chromatography/high-resolution accurate mass spectrometry (LC/HRAM-MS) platform for the analysis of 3R4F-derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.

RESULTS

A total of 331 compounds with semi-quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM-MS-based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non-targeted screening (NTS) approach.

CONCLUSIONS

This study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

Metabolomics Study of the Synergistic Killing of Polymyxin B in Combination with Amikacin against Polymyxin-Susceptible and -Resistant Pseudomonas aeruginosa

In the present study, we employed untargeted metabolomics to investigate the synergistic killing mechanism of polymyxin B in combination with an aminoglycoside, amikacin, against a polymyxin-susceptible isolate of Pseudomonas aeruginosa, FADDI-PA111 (MIC = 2 mg/liter for both polymyxin B and amikacin), and a polymyxin-resistant Liverpool epidemic strain (LES), LESB58 (the corresponding MIC for both polymyxin B and amikacin is 16 mg/liter). The metabolites were extracted 15 min, 1 h, and 4 h following treatment with polymyxin B alone (2 mg/liter for FADDI-PA111; 4 mg/liter for LESB58), amikacin alone (2 mg/liter), and both in combination and analyzed using liquid chromatography-mass spectrometry (LC-MS). At 15 min and 1 h, polymyxin B alone induced significant perturbations in glycerophospholipid and fatty acid metabolism pathways in FADDI-PA111 and, to a lesser extent, in LESB58. Amikacin alone at 1 and 4 h induced significant perturbations in peptide and amino acid metabolism, which is in line with the mode of action of aminoglycosides. Pathway analysis of FADDI-PA111 revealed that the synergistic effect of the combination was largely due to the inhibition of cell envelope biogenesis, which was driven initially by polymyxin B via suppression of key metabolites involved in lipopolysaccharide, peptidoglycan, and membrane lipids (15 min and 1 h) and later by amikacin (4 h). Overall, these novel findings demonstrate that the disruption of cell envelope biogenesis and central carbohydrate metabolism, decreased levels of amino sugars, and a downregulated nucleotide pool are the metabolic pathways associated with the synergistic killing of the polymyxin-amikacin combination against P. aeruginosa This mechanistic study might help optimize synergistic polymyxin B combinations in the clinical setting.

CD9 identifies pancreatic cancer stem cells and modulates glutamine metabolism to fuel tumour growth

Pancreatic ductal adenocarcinoma (PDAC) shows great cellular heterogeneity, with pronounced epithelial and mesenchymal cancer cell populations. However, the cellular hierarchy underlying PDAC cell diversity is unknown. Here we identify the tetraspanin CD9 as a marker of PDAC tumour-initiating cells. CD9^high cells had increased organoid formation capability, and generated tumour grafts in vivo at limiting dilutions. Tumours initiated from CD9^high cells recapitulated the cellular heterogeneity of primary PDAC, whereas CD9^low cells only produced duct-like epithelial progeny. CD9 knockdown decreased the growth of PDAC organoids, and heterozygous CD9 deletion in Pdx1-Cre; LSL-KRas^G12D; p53^F/F mice prolonged overall survival. Mechanistically, CD9 promoted the plasma membrane localisation of the glutamine transporter ASCT2, enhancing glutamine uptake in PDAC cells. Thus, our study identifies a PDAC subpopulation capable of initiating PDAC and giving rise to PDAC heterogeneity, suggesting that the cellular diversity of PDAC is generated by PDAC stem cell differentiation.

Creating a Reliable Mass Spectral-Retention Time Library for All Ion Fragmentation-Based Metabolomics

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC-MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC-MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H₂O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

Mass Spectrometry Fingerprints of Small-Molecule Metabolites in Biofluids: Building a Spectral Library of Recurrent Spectra for Urine Analysis

A large fraction of ions observed in electrospray liquid chromatography-mass spectrometry (LC-ESI-MS) experiments of biological samples remain unidentified. One of the main reasons for this is that spectral libraries of pure compounds fail to account for the complexity of the metabolite profiling of complex materials. Recently, the NIST Mass Spectrometry Data Center has been developing a novel type of searchable mass spectral library that includes all recurrent unidentified spectra found in the sample profile. These libraries, in conjunction with the NIST tandem mass spectral library, allow analysts to explore most of the chemical space accessible to LC-MS analysis. In this work, we demonstrate how these libraries can provide a reliable fingerprint of the material by applying them to a variety of urine samples, including an extremely altered urine from cancer patients undergoing total body irradiation. The same workflow is applicable to any other biological fluid. The selected class of acylcarnitines is examined in detail, and derived libraries and related software are freely available. They are intended to serve as online resources for continuing community review and improvement.

Functional cross-talk between allosteric effects of activating and inhibiting ligands underlies PKM2 regulation

Several enzymes can simultaneously interact with multiple intracellular metabolites, however, how the allosteric effects of distinct ligands are integrated to coordinately control enzymatic activity remains poorly understood. We addressed this question using, as a model system, the glycolytic enzyme pyruvate kinase M2 (PKM2). We show that the PKM2 activator fructose 1,6-bisphosphate (FBP) alone promotes tetramerisation and increases PKM2 activity, but addition of the inhibitor L-phenylalanine (Phe) prevents maximal activation of FBP-bound PKM2 tetramers. We developed a method, AlloHubMat, that uses eigenvalue decomposition of mutual information derived from molecular dynamics trajectories to identify residues that mediate FBP-induced allostery. Experimental mutagenesis of these residues identified PKM2 variants in which activation by FBP remains intact but cannot be attenuated by Phe. Our findings reveal residues involved in FBP-induced allostery that enable the integration of allosteric input from Phe and provide a paradigm for the coordinate regulation of enzymatic activity by simultaneous allosteric inputs.

Evaluation of an untargeted nano-liquid chromatography-mass spectrometry approach to expand coverage of low molecular weight dissolved organic matter in Arctic soil

Characterizing low molecular weight (LMW) dissolved organic matter (DOM) in soils and evaluating the availability of this labile pool is critical to understanding the underlying mechanisms that control carbon storage or release across terrestrial systems. However, due to wide-ranging physicochemical diversity, characterizing this complex mixture of small molecules and how it varies across space remains an analytical challenge. Here, we evaluate an untargeted approach to detect qualitative and relative-quantitative variations in LMW DOM with depth using water extracts from a soil core from the Alaskan Arctic, a unique system that contains nearly half the Earth's terrestrial carbon and is rapidly warming due to climate change. We combined reversed-phase and hydrophilic interaction liquid chromatography, and nano-electrospray ionization coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization mode. The optimized conditions were sensitive, robust, highly complementary, and enabled detection and putative annotations of a wide range of compounds (e.g. amino acids, plant/microbial metabolites, sugars, lipids, peptides). Furthermore, multivariate statistical analyses revealed subtle but consistent and significant variations with depth. Thus, this platform is useful not only for characterizing LMW DOM, but also for quantifying relative variations in LMW DOM availability across space, revealing hotspots of biogeochemical activity for further evaluation.

Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art

Liquid chromatography (LC) hyphenated to mass spectrometry is currently the most widely used means of determining metabolic phenotypes via both untargeted and targeted analysis. At present a range of analytical separations, including reversed-phase, hydrophilic interaction and ion-pair LC are employed to maximise metabolome coverage with ultra (high) performance liquid chromatography (UHPLC) increasingly displacing conventional high performance liquid chromatography because of the need for short analysis times and high peak capacity in such applications. However, it is widely recognized that these methodologies do not entirely solve the problems facing researchers trying to perform comprehensive metabolic phenotyping and in addition to these "routine" approaches there are continuing investigations of alternative separation methods including 2-dimensional/multi column approaches. These involve either new stationary phases or multidimensional combinations of the more conventional materials currently used, as well as application of miniaturization or "new" approaches such as supercritical HP and UHP- chromatographic separations. There is also a considerable amount of interest in the combination of chromatographic and ion mobility separations, with the latter providing both an increase in resolution and the potential to provide additional structural information via the determination of molecular collision cross section data. However, key problems remain to be solved including ensuring quality, comparability across different laboratories and the ever present difficulty of identifying unknowns.

Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

BACKGROUND

The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.

OBJECTIVE

To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.

METHODS

A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.

RESULTS

As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.

CONCLUSION

Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.

LC-ESI/LTQOrbitrap/MS/MS and GC-MS profiling of Stachys parviflora L. and evaluation of its biological activities

The use of some Stachys genus as herbal remedies is known and the aerial parts have a pharmaceutical interest, being used in Anatolia and Iran as wild tea. In this study, chemical composition, antimicrobial, antioxidant, and antiproliferative activities of the methanolic extract and essential oil (EO) of Stachys parviflora L. (S. parviflora) were evaluated. Qualitative analysis of metabolites of S. parviflora methanolic extract was studied using liquid chromatography coupled to high resolution mass spectrometry (LC-ESI/LTQOrbitrap/MS), evidencing the presence of phenolic acids and flavonoids derivatives. The EO was analyzed using gas chromatography coupled to mass spectrometry (GC/MS). Eighty-seven compounds were characterized in the EO of S. parviflora, of which α-terpenyl acetate (23.6%), β-caryophyllene (16.8%), bicyclogermacrene (9.3%), spathulenol (4.9%) and α-pinene (4.2%) were found to be the major components. The highest antimicrobial effect of EO was found to S. aureus and B. cereus (MIC = 0.01 μg/ml), while the highest activity of extract was against B. cereus (MIC = 125 μg/ml). The methanolic extract exhibited strong antioxidant activity in DPPH (IC₅₀ = 76.87 μg/ml) and β-carotene/linoleic acid assay (BCB, IC₅₀ = 188.47 μg/ml) methods. Furthermore, in vitro cytotoxicity evaluation against three cell lines namely human ovarian carcinoma (A2780), human colon carcinoma (HCT), and mouse melanoma cell line (B16F10), showed an anti-proliferative activity of the EO ranging from IC₅₀ value 30.95 μg/ml to 16.55 μg/ml. The results from this study have demonstrated the promising cytotoxic, antibacterial, and antifungal properties of S. parviflora, which could have wide potential applications in food and pharmaceutical industries.

A scoring approach for multi-platform acquisition in metabolomics

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC-MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC-MS conditions were used.

Global Metabolic Analyses of Acinetobacter baumannii

Acinetobacter baumannii is rapidly emerging as a multidrug-resistant pathogen responsible for nosocomial infections including pneumonia, bacteremia, wound infections, urinary tract infections, and meningitis. Metabolomics provides a powerful tool to gain a system-wide snapshot of cellular biochemical networks under defined conditions and has been increasingly applied to bacterial physiology and drug discovery. Here we describe an optimized sample preparation method for untargeted metabolomics studies in A. baumannii. Our method provides a significant recovery of intracellular metabolites to demonstrate substantial differences in global metabolic profiles among A. baumannii strains.

Metabolomics Platform with Capillary Electrophoresis Coupled with High-Resolution Mass Spectrometry for Plasma Analysis

Metabolome analysis using capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) has the potential to improve coverage of metabolite detection because of its high selectivity and sensitivity. Configuration of the interface between CE and HRMS to meet the ground connection is essential for enabling independent regulation of the electrical currents in the CE and electrospray field. In the present study, we applied an electrospray-ionization adapter equipped with a grounded nebulizer to CE-HRMS and tested the analytical performance for 34 charged compounds. The extracted-ion electropherograms, consisting of seven sets of isomers, showed reasonable peak shapes and separation for the annotation of each metabolite. The levels of 34 target analytes in a standard mixture were determined with a dynamic range of at least 10², maintaining linearity with r² > 0.9. The repeatability and intermediate precision above the lower limit of quantification showed the relative standard deviation to be lower than 20%. In the spike-recovery experiment, 27 of the 34 metabolites in plasma extract were recovered at a rate of 80 to 120%, suggesting high accuracy. Furthermore, we assessed the feasibility of our platform in metabolome analysis using human-plasma extract. The results showed successful detection of 270 metabolites, indicating the potential of our platform to yield higher coverage of the metabolome. In addition, analysis of dilution integrity demonstrated the quantitative ability of metabolome analysis with CE-HRMS, although the existence of saturation or matrix effects were seen in the case of 33 of the metabolites. This study indicates that our platform has great potential for large-scale metabolome analysis of plasma for biological studies and clinical biomarker screening.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

MCT2 mediates concentration-dependent inhibition of glutamine metabolism by MOG

α-Ketoglutarate (αKG) is a key node in many important metabolic pathways. The αKG analog N-oxalylglycine (NOG) and its cell-permeable prodrug dimethyloxalylglycine (DMOG) are extensively used to inhibit αKG-dependent dioxygenases. However, whether NOG interference with other αKG-dependent processes contributes to its mode of action remains poorly understood. Here we show that, in aqueous solutions, DMOG is rapidly hydrolyzed, yielding methyloxalylglycine (MOG). MOG elicits cytotoxicity in a manner that depends on its transport by monocarboxylate transporter 2 (MCT2) and is associated with decreased glutamine-derived tricarboxylic acid-cycle flux, suppressed mitochondrial respiration and decreased ATP production. MCT2-facilitated entry of MOG into cells leads to sufficiently high concentrations of NOG to inhibit multiple enzymes in glutamine metabolism, including glutamate dehydrogenase. These findings reveal that MCT2 dictates the mode of action of NOG by determining its intracellular concentration and have important implications for the use of (D)MOG in studying αKG-dependent signaling and metabolism.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

Metabolic oscillations on the circadian time scale in Drosophila cells lacking clock genes

Circadian rhythms are cell‐autonomous biological oscillations with a period of about 24 h. Current models propose that transcriptional feedback loops are the primary mechanism for the generation of circadian oscillations. Within this framework, Drosophila S2 cells are regarded as “non‐rhythmic” cells, as they do not express several canonical circadian components. Using an unbiased multi‐omics approach, we made the surprising discovery that Drosophila S2 cells do in fact display widespread daily rhythms. Transcriptomics and proteomics analyses revealed that hundreds of genes and their products, and in particular metabolic enzymes, are rhythmically expressed in a 24‐h cycle. Metabolomics analyses extended these findings and demonstrate that central carbon metabolism and amino acid metabolism are core metabolic pathways driven by protein rhythms. We thus demonstrate that 24‐h metabolic oscillations, coupled to gene and protein cycles, take place in nucleated cells without the contribution of any known circadian regulators. These results therefore suggest a reconsideration of existing models of the clockwork in Drosophila and other eukaryotic systems.

Host: Microbiome co-metabolic processing of dietary polyphenols - An acute, single blinded, cross-over study with different doses of apple polyphenols in healthy subjects

Apples are one of the most commonly consumed fruits and their high polyphenol content is considered one of the most important determinants of their health-promoting activities. Here we studied the nutrikinetics of apple polyphenols by UHPLC-HRMS metabolite fingerprinting, comparing bioavailability when consumed in a natural or a polyphenol-enriched cloudy apple juice. Twelve men and women participated in an acute single blind controlled crossover study in which they consumed 250 mL of cloudy apple juice (CAJ), Crispy Pink apple variety, or 250 mL of the same juice enriched with 750 mg of an apple polyphenol extract (PAJ). Plasma and whole blood were collected at time 0, 1, 2, 3 and 5 h. Urine was collected at time 0 and 0-2, 2-5, 5-8, and 8-24 h after juice consumption. Faecal samples were collected from each individual during the study for 16S rRNA gene profiling. As many as 110 metabolites were significantly elevated following intake of polyphenol enriched cloudy apple juice, with large inter-individual variations. The comparison of the average area under the curve of circulating metabolites in plasma and in urine of volunteers consuming either the CAJ or the PAJ demonstrated a stable metabotype, suggesting that an increase in polyphenol concentration in fruit does not limit their bioavailability upon ingestion. Faecal bacteria were correlated with specific microbial catabolites derived from apple polyphenols. Human metabolism of apple polyphenols is a co-metabolic process between human encoded activities and those of our resident microbiota. Here we have identified specific blood and urine metabolic biomarkers of apple polyphenol intake and identified putative associations with specific genera of faecal bacteria, associations which now need confirmation in specifically designed mechanistic studies.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.